lfm2.5-Encoder-350M-datause

Fine-tune of LiquidAI/LFM2.5-Encoder-350M for BIO data-use mention tagging (dataset / survey / census / registry mentions in economics research papers).

Labels

• NAMED_DATA — a proper name, title, or acronym of a specific data source
• DESCRIPTIVE_DATA — a source described in words but not named
• VAGUE_DATA — generic data wording with no identifiable source

Training

• base model: LiquidAI/LFM2.5-Encoder-350M
• dataset: rafmacalaba/data-use-mentions (bio config)
• epochs: 5
• learning rate: 2e-05
• batch size: 16
• precision: bf16

Evaluation (holdout, label-agnostic)

thr	tp	fp	fn	precision	recall	f0.5	f1
0.10	4577	1091	2769	0.8075	0.6231	0.7624	0.7034
0.20	4577	1091	2769	0.8075	0.6231	0.7624	0.7034
0.30	4576	1091	2770	0.8075	0.6229	0.7623	0.7033
0.40	4565	1086	2781	0.8078	0.6214	0.7621	0.7025
0.50	4501	1033	2845	0.8133	0.6127	0.7633	0.6989
0.60	4200	840	3146	0.8333	0.5717	0.7635	0.6782
0.70	3877	687	3469	0.8495	0.5278	0.7572	0.6510

Best F0.5: 0.7635 (thr=0.6) Best F1: 0.7034 (thr=0.1)